In [1]:
import warnings
warnings.filterwarnings('ignore')
import geopandas as gpd
import pandas as pd
import contextily as ctx
import folium
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.patheffects as pe
from pointpats import centrography
import seaborn as sns
import calendar
import branca.colormap as cm
In [2]:
# Import preprocessed data
folder_path = "data/final_processed_data/"
neighborhood_stats = gpd.read_file(folder_path + "neighborhood_stats.gpkg")
airbnb_gdf = gpd.read_file(folder_path + "airbnb_listings.gpkg")
airbnb_prices = gpd.read_file(folder_path + "airbnb_prices.gpkg")
airbnb_prices_entirespace = gpd.read_file(folder_path + "airbnb_prices_entirespace.gpkg")
airbnb_prices_entirespace_highseason = gpd.read_file(folder_path + "airbnb_prices_entirespace_highseason.gpkg")
airbnb_prices_entirespace_lowseason = gpd.read_file(folder_path + "airbnb_prices_entirespace_lowseason.gpkg")
poi_gdf = gpd.read_file(folder_path + "tourism_pois.gpkg")
museums_gdf = gpd.read_file(folder_path + "museum_pois.gpkg")
galleries_gdf = gpd.read_file(folder_path + "gallery_pois.gpkg")
monuments_gdf = gpd.read_file(folder_path + "monument_pois.gpkg")
buurten_gdf = gpd.read_file(folder_path + "neighborhood_polygons.gpkg")
if neighborhood_stats.crs != "EPSG:28992" or airbnb_gdf.crs != "EPSG:28992" or airbnb_prices.crs != "EPSG:28992" or airbnb_prices_entirespace.crs != "EPSG:28992" or airbnb_prices_entirespace_highseason.crs != "EPSG:28992" or airbnb_prices_entirespace_lowseason.crs != "EPSG:28992" or poi_gdf.crs != "EPSG:28992" or museums_gdf.crs != "EPSG:28992" or galleries_gdf.crs != "EPSG:28992" or monuments_gdf.crs != "EPSG:28992" or buurten_gdf.crs != "EPSG:28992":
print("Convert all datasets to EPSG:28992")
else:
print("All dataset CRS are set to EPSG:28992")
All dataset CRS are set to EPSG:28992
Census neighborhood polygons¶
In [3]:
# Contextily basemaps
# Reproject for contextily
buurten_3857 = buurten_gdf.to_crs(epsg=3857)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
# Amsterdam map
buurten_3857.plot(ax=ax1,edgecolor="yellow",facecolor="red",alpha=0.2,linewidth=0.5)
ctx.add_basemap(ax1,source=ctx.providers.Esri.WorldImagery,crs=buurten_3857.crs)
ax1.set_title("Amsterdam Census Neighborhoods", fontsize=12)
ax1.set_axis_off()
# Netherlands Map
amsterdam_point = buurten_3857.unary_union.centroid
x_pt, y_pt = amsterdam_point.x, amsterdam_point.y
margin = 750_000
ax2.set_xlim(x_pt - margin, x_pt + margin)
ax2.set_ylim(y_pt - margin, y_pt + margin)
ctx.add_basemap(ax2,source=ctx.providers.CartoDB.Voyager,crs=buurten_3857.crs)
ax2.scatter(x_pt,y_pt,s=150,marker="*",color="red",edgecolor="black",linewidth=1,zorder=5)
ax2.set_title("Amsterdam Location", fontsize=12)
ax2.set_axis_off()
plt.tight_layout()
plt.show()
Nationally Registered Monuments¶
In [4]:
# Joint Plot Map
monuments_gdf_3857 = monuments_gdf.to_crs(epsg=3857)
monuments_gdf_3857["longitude"] = monuments_gdf_3857.geometry.x
monuments_gdf_3857["latitude"] = monuments_gdf_3857.geometry.y
mean_center = centrography.mean_center(monuments_gdf_3857[["longitude", "latitude"]])
med_center = centrography.euclidean_median(monuments_gdf_3857[["longitude", "latitude"]])
# Generate scatterplot
joint_axes = sns.jointplot(x="longitude", y="latitude", data=monuments_gdf_3857, s=0.75, height=9)
# Add mean point and marginal lines
joint_axes.ax_joint.scatter(*mean_center, color="red", marker="x", s=50, label="Mean Center")
joint_axes.ax_marg_x.axvline(mean_center[0], color="red")
joint_axes.ax_marg_y.axhline(mean_center[1], color="red")
# Add median point and marginal lines
joint_axes.ax_joint.scatter(*med_center,color="limegreen",marker="o",s=50,label="Median Center")
joint_axes.ax_marg_x.axvline(med_center[0], color="limegreen")
joint_axes.ax_marg_y.axhline(med_center[1], color="limegreen")
# Legend
joint_axes.ax_joint.legend()
# Add basemap
ctx.add_basemap(joint_axes.ax_joint, source=ctx.providers.CartoDB.Positron)
# Clean axes
joint_axes.ax_joint.set_axis_off()
# Display
plt.show()
In [5]:
# Hexbin map
# Set up figure and axis
f, ax = plt.subplots(1, figsize=(12, 9))
hb = ax.hexbin(monuments_gdf_3857["longitude"],monuments_gdf_3857["latitude"],gridsize=50,linewidths=0,alpha=0.5,cmap="viridis_r",)
# Add basemap
ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron)
# Add colorbar
plt.colorbar(hb)
# Remove axes
ax.set_axis_off()
In [6]:
# Leaflet interactive map
monuments_wgs = monuments_gdf.to_crs(epsg=4326).copy()
center = [monuments_wgs.geometry.y.mean(),monuments_wgs.geometry.x.mean()]
interactive_monuments_map = folium.Map(location=center, zoom_start=12, tiles="CartoDB positron")
for _, row in monuments_wgs.iterrows():
geom = row.geometry
if geom is None:
continue
lat = geom.y
lon = geom.x
herkomst = row.get("herkomst", "NA")
subcategor = row.get("subcategor", "NA")
tooltip_text = f"Origin: {herkomst}<br>Subcategory: {subcategor}"
folium.CircleMarker(location=[lat, lon],radius=1.5,color="orange",fill=True,fill_color="orange",popup=tooltip_text,tooltip=tooltip_text).add_to(interactive_monuments_map)
interactive_monuments_map
Out[6]:
Make this Notebook Trusted to load map: File -> Trust Notebook
Tourism POI Data¶
In [7]:
# Joint plot map
poi_gdf_3857 = poi_gdf.to_crs(epsg=3857)
poi_gdf_3857["longitude"] = poi_gdf_3857.geometry.x
poi_gdf_3857["latitude"] = poi_gdf_3857.geometry.y
mean_center = centrography.mean_center(poi_gdf_3857[["longitude", "latitude"]])
med_center = centrography.euclidean_median(poi_gdf_3857[["longitude", "latitude"]])
# Generate scatterplot
joint_axes = sns.jointplot(x="longitude", y="latitude", data=poi_gdf_3857, s=0.75, height=9)
# Add mean point and marginal lines
joint_axes.ax_joint.scatter(*mean_center, color="red", marker="x", s=50, label="Mean Center")
joint_axes.ax_marg_x.axvline(mean_center[0], color="red")
joint_axes.ax_marg_y.axhline(mean_center[1], color="red")
# Add median point and marginal lines
joint_axes.ax_joint.scatter(*med_center,color="limegreen",marker="o",s=50,label="Median Center")
joint_axes.ax_marg_x.axvline(med_center[0], color="limegreen")
joint_axes.ax_marg_y.axhline(med_center[1], color="limegreen")
# Legend
joint_axes.ax_joint.legend()
# Add basemap
ctx.add_basemap(joint_axes.ax_joint, source=ctx.providers.CartoDB.Positron)
# Clean axes
joint_axes.ax_joint.set_axis_off()
# Display
plt.show()
In [8]:
# Hexbin map
# Set up figure and axis
f, ax = plt.subplots(1, figsize=(12, 9))
hb = ax.hexbin(poi_gdf_3857["longitude"],poi_gdf_3857["latitude"],gridsize=50,linewidths=0,alpha=0.5,cmap="viridis_r",)
# Add basemap
ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron)
# Add colorbar
plt.colorbar(hb)
# Remove axes
ax.set_axis_off()
In [9]:
# Leaflet interactive map
poi_wgs = poi_gdf.to_crs(epsg=4326).copy()
center = [poi_wgs.geometry.y.mean(),poi_wgs.geometry.x.mean()]
interactive_poi_map = folium.Map(location=center, zoom_start=12, tiles="CartoDB positron")
for _, row in poi_wgs.iterrows():
geom = row.geometry
if geom is None:
continue
lat = geom.y
lon = geom.x
name = row.get("name", "NA")
tourism = row.get("tourism", "NA")
tooltip_text = f"Name: {name}<br>Tourism: {tourism}"
folium.CircleMarker(location=[lat, lon],radius=1.5,color="purple",fill=True,fill_color="purple",popup=tooltip_text,tooltip=tooltip_text).add_to(interactive_poi_map)
interactive_poi_map
Out[9]:
Make this Notebook Trusted to load map: File -> Trust Notebook
Monuments, Museums, Galleries¶
In [10]:
# Chroropleth trio
# Columns to plot
cols = ["count_monuments", "count_museums", "count_galleries"]
titles = ["Monuments Count per neighborhood", "Museums Count per neighborhood", "Galleries Count per neighborhood"]
# Create a 1x3 grid of maps (three columns)
fig, axes = plt.subplots(1, 3, figsize=(18, 6))
# Plot each column in a separate map
for ax, col, title in zip(axes, cols, titles):
vmin = neighborhood_stats[col].min()
vmax = neighborhood_stats[col].max()
neighborhood_stats.plot(column=col,cmap="YlGnBu",edgecolor="black",linewidth=0.3,legend=True,vmin=vmin,vmax=vmax,ax=ax)
ax.set_title(title, fontsize=14)
ax.set_axis_off()
# Adjust layout and display
plt.tight_layout()
plt.show()
Airbnb Data¶
In [11]:
# joint plot
airbnb_gdf_3857 = airbnb_gdf.to_crs(epsg=3857)
airbnb_gdf_3857["longitude"] = airbnb_gdf_3857.geometry.x
airbnb_gdf_3857["latitude"] = airbnb_gdf_3857.geometry.y
mean_center = centrography.mean_center(airbnb_gdf_3857[["longitude", "latitude"]])
med_center = centrography.euclidean_median(airbnb_gdf_3857[["longitude", "latitude"]])
# Generate scatterplot
joint_axes = sns.jointplot(x="longitude", y="latitude", data=airbnb_gdf_3857, s=0.75, height=9)
# Add mean point and marginal lines
joint_axes.ax_joint.scatter(*mean_center, color="red", marker="x", s=50, label="Mean Center")
joint_axes.ax_marg_x.axvline(mean_center[0], color="red")
joint_axes.ax_marg_y.axhline(mean_center[1], color="red")
# Add median point and marginal lines
joint_axes.ax_joint.scatter(*med_center,color="limegreen",marker="o",s=50,label="Median Center")
joint_axes.ax_marg_x.axvline(med_center[0], color="limegreen")
joint_axes.ax_marg_y.axhline(med_center[1], color="limegreen")
# Legend
joint_axes.ax_joint.legend()
# Add basemap
ctx.add_basemap(joint_axes.ax_joint, source=ctx.providers.CartoDB.Positron)
# Clean axes
joint_axes.ax_joint.set_axis_off()
# Display
plt.show()
In [12]:
# Kernel density map
f, ax = plt.subplots(1, figsize=(9, 9))
sns.kdeplot(x="longitude",y="latitude",data=airbnb_gdf_3857,n_levels=50,shade=True,alpha=0.33,cmap="viridis_r")
ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron)
ax.set_axis_off()
In [13]:
# Choropleth map of airbnb count per neighborhood
neighborhood_stats_3857 = neighborhood_stats.to_crs(epsg=3857)
fig, ax = plt.subplots(figsize=(10, 8))
neighborhood_stats_3857.plot(column="count_airbnbs",cmap="Blues",edgecolor="black",linewidth=0.3,legend=True,ax=ax)
ctx.add_basemap(ax,source=ctx.providers.CartoDB.PositronNoLabels,crs=neighborhood_stats_3857.crs)
ax.set_title("Number of Airbnbs per Neighborhood", fontsize=14)
ax.set_axis_off()
plt.tight_layout()
plt.show()
In [14]:
# Leaflet interactive map of airbnb listings
airbnb_wgs = airbnb_prices.to_crs(epsg=4326).copy()
airbnb_wgs["price"] = pd.to_numeric(airbnb_wgs["price"], errors="coerce")
if "last_review" in airbnb_wgs.columns:
airbnb_wgs["last_review"] = pd.to_datetime(airbnb_wgs["last_review"], errors="coerce")
center = [airbnb_wgs.geometry.y.mean(),airbnb_wgs.geometry.x.mean()]
interactive_price_map = folium.Map(location=center, zoom_start=12, tiles="CartoDB positron")
vmin = airbnb_wgs["price"].min(skipna=True)
vmax = airbnb_wgs["price"].max(skipna=True)
colormap = cm.linear.Greens_09.scale(vmin, vmax)
for _, row in airbnb_wgs.iterrows():
geom = row.geometry
if geom is None:
continue
lat = geom.y
lon = geom.x
price = row["price"]
# Extra fields for tooltip
neighbourhood = row.get("neighbourhood", "NA")
room_type = row.get("room_type", "NA")
last_review = row.get("last_review", pd.NA)
if pd.isna(last_review):
last_review_str = "NA"
else:
last_review_str = str(last_review).split(" ")[0]
color = colormap(price)
price_str = f"{price:.0f}"
tooltip_text = (f"Neighborhood: {neighbourhood}<br>"
f"Room type: {room_type}<br>"
f"Last review: {last_review_str}<br>"
f"Price: €{price_str}")
folium.CircleMarker(location=[lat, lon],radius=3,fill=True,fill_opacity=0.9,color=None,fill_color=color,tooltip=tooltip_text).add_to(interactive_price_map)
colormap.caption = "Airbnb price (darker green = more expensive)"
colormap.add_to(interactive_price_map)
interactive_price_map
Out[14]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [15]:
# Choropleth quad for airbnb prices
cols = ["avg_price_airbnb","avg_price_airbnb_entirespace","avg_price_airbnb_entirespace_highseason","avg_price_airbnb_entirespace_lowseason",]
titles = ["Avg price – all Airbnbs","Avg price – entire place","Avg price – entire place (high season)","Avg price – entire place (low season)",]
# Shared color scale across all four maps
vmin = min(neighborhood_stats[c].min() for c in cols)
vmax = max(neighborhood_stats[c].max() for c in cols)
fig, axes = plt.subplots(2, 2, figsize=(14, 12))
for ax, col, title in zip(axes.flat, cols, titles):
neighborhood_stats.plot(column=col,cmap="Greens",edgecolor="black",linewidth=0.2,legend=True,vmin=vmin,vmax=vmax,ax=ax)
ax.set_title(title, fontsize=11)
ax.set_axis_off()
plt.tight_layout()
plt.show()
In [16]:
# tourism visitors stats
visitorstats_df = pd.read_csv("data/tourism_statistics/amsterdam_tourist_arrivals.csv")
## Look at Amsterdam tourism arrivals to determine high season vs low season
visitorstats_df = visitorstats_df.drop(columns=["Unnamed: 12"], errors="ignore")
visitorstats_long = visitorstats_df.melt(var_name="Month", value_name="Visitors")
visitorstats_long["Visitors"] = pd.to_numeric(visitorstats_long["Visitors"], errors="coerce")
month_order = ["01-Jan", "02-Feb", "03-Mar", "04-Apr", "05-May", "06-Jun","07-Jul", "08-Aug", "09-Sep", "10-Oct", "11-Nov", "12-Dec"]
visitorstats_long["Month"] = pd.Categorical(visitorstats_long["Month"], categories=month_order, ordered=True)
visitorstats_long = visitorstats_long.sort_values("Month")
plt.figure(figsize=(10, 5))
plt.plot(visitorstats_long["Month"].astype(str),visitorstats_long["Visitors"],marker="o", color="orange", linewidth=2)
# annotate with note about King's Day & Tulip Bloom
apr_mask = visitorstats_long["Month"] == "04-Apr"
x_apr = visitorstats_long.loc[apr_mask, "Month"].iloc[0]
y_apr = visitorstats_long.loc[apr_mask, "Visitors"].iloc[0]
plt.annotate("King's Day & Tulip Bloom Window",xy=(x_apr, y_apr),xytext=(0, 20),textcoords="offset points",arrowprops=dict(arrowstyle="->", linewidth=1),ha="center")
# annotate with note about October ADE
oct_mask = visitorstats_long["Month"] == "10-Oct"
x_oct = visitorstats_long.loc[oct_mask, "Month"].astype(str).iloc[0]
y_oct = visitorstats_long.loc[oct_mask, "Visitors"].iloc[0]
plt.annotate("ADE Festival (October)",xy=(x_oct, y_oct),xytext=(0, 20),textcoords="offset points",arrowprops=dict(arrowstyle="->", linewidth=1),ha="center")
plt.title("Monthly Tourism Arrivals")
plt.xlabel("Month")
plt.ylabel("Number of Visitors (thousands)")
plt.grid(True, linestyle="--", alpha=0.5)
plt.tight_layout()
plt.show()
In [17]:
# Calculate average airbnb price per month
# Group by month and calculate mean price
avg_price_by_month = (airbnb_prices_entirespace.groupby("review_month")["price"].mean().reset_index().sort_values("review_month"))
print(avg_price_by_month)
# Calculate average price per year
avg_price_by_year = (airbnb_prices_entirespace.groupby("review_year")["price"].mean().reset_index().sort_values("review_year"))
print(avg_price_by_year)
avg_price_by_month["month_name"] = avg_price_by_month["review_month"].apply(lambda x: calendar.month_name[int(x)] if pd.notna(x) else None)
# Plot prices by month
plt.figure(figsize=(9, 5))
plt.plot(avg_price_by_month["review_month"],avg_price_by_month["price"],marker="o",color="green",linewidth=2)
# annotate with note about King's Day & Tulip Bloom
apr_mask = avg_price_by_month["review_month"] == 4
x_apr = avg_price_by_month.loc[apr_mask, "review_month"].iloc[0]
y_apr = avg_price_by_month.loc[apr_mask, "price"].iloc[0]
plt.annotate("King's Day & Tulip Bloom Window",xy=(x_apr, y_apr),xytext=(0, 20),textcoords="offset points",arrowprops=dict(arrowstyle="->", linewidth=1),ha="center")
# annotate with note about October ADE
oct_mask = avg_price_by_month["review_month"] == 10
x_oct = avg_price_by_month.loc[oct_mask, "review_month"].iloc[0]
y_oct = avg_price_by_month.loc[oct_mask, "price"].iloc[0]
plt.annotate("ADE Festival (October)",xy=(x_oct, y_oct),xytext=(0, 20),textcoords="offset points",arrowprops=dict(arrowstyle="->", linewidth=1),ha="center")
plt.title("Average Airbnb Price per Month")
plt.xlabel("Month")
plt.ylabel("Average Price (€)")
plt.xticks(avg_price_by_month["review_month"],avg_price_by_month["month_name"],rotation=45)
plt.grid(True, linestyle="--", alpha=0.5)
plt.show()
review_month price 0 1.0 242.390244 1 2.0 219.630435 2 3.0 210.909091 3 4.0 265.990536 4 5.0 248.368957 5 6.0 259.353808 6 7.0 276.509804 7 8.0 257.535805 8 9.0 250.720617 9 10.0 273.420000 10 11.0 236.142857 11 12.0 227.875000 review_year price 0 2017.0 218.833333 1 2018.0 230.142857 2 2019.0 262.529412 3 2020.0 248.000000 4 2021.0 184.555556 5 2022.0 279.829268 6 2023.0 267.417266 7 2024.0 256.098876 8 2025.0 257.691436
In [18]:
# Calculate airbnb prices by seasonality
print("Number of entire home listings in high season: " + str(airbnb_prices_entirespace_highseason['price'].count()))
print("Number of entire home listings in low season: " + str(airbnb_prices_entirespace_lowseason['price'].count()))
plt.figure(figsize=(8, 5))
sns.kdeplot(data=airbnb_prices_entirespace_highseason,x="price",linewidth=2,label="High season")
sns.kdeplot(data=airbnb_prices_entirespace_lowseason,x="price",linewidth=2,label="Low season")
high_mean_price = airbnb_prices_entirespace_highseason["price"].mean()
low_mean_price = airbnb_prices_entirespace_lowseason["price"].mean()
plt.axvline(high_mean_price, color="blue", linestyle="--", linewidth=1.5, label=f"Mean High Season (€{high_mean_price:.0f})")
plt.axvline(low_mean_price, color="orange", linestyle="-.", linewidth=1.5, label=f"Mean Low Season (€{low_mean_price:.0f})")
plt.title("Airbnb Price Distribution: High vs Low Season")
plt.xlabel("Price per night (€)")
plt.ylabel("Density")
plt.xlim(left=0)
plt.legend()
plt.show()
Number of entire home listings in high season: 2914 Number of entire home listings in low season: 761
In [19]:
# point maps of high vs low season airbnb prices
high_3857 = airbnb_prices_entirespace_highseason.to_crs(epsg=3857)
low_3857 = airbnb_prices_entirespace_lowseason.to_crs(epsg=3857)
vmin = min(high_3857["price"].min(), low_3857["price"].min())
vmax = max(high_3857["price"].max(), low_3857["price"].max())
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
# High season prices
high_3857.plot(ax=ax1,column="price",cmap="OrRd",markersize=6,alpha=0.9,legend=True,vmin=vmin,vmax=vmax)
ctx.add_basemap(ax1, source=ctx.providers.CartoDB.Positron, crs=high_3857.crs)
ax1.set_title("Airbnb Prices – High season", fontsize=12)
ax1.set_axis_off()
# Low season prices
low_3857.plot(ax=ax2,column="price",cmap="OrRd",markersize=6,alpha=0.9,legend=True,vmin=vmin,vmax=vmax)
ctx.add_basemap(ax2, source=ctx.providers.CartoDB.Positron, crs=low_3857.crs)
ax2.set_title("Airbnb Prices – Low season", fontsize=12)
ax2.set_axis_off()
plt.tight_layout()
plt.show()
In [ ]: